Stride width is vital for gait stability, postural balance control, and
fall risk reduction. However, estimating stride width typically requires
either grounded cameras or a full kinematic body suit of wearable
inertial measurement units (IMUs), both of which are often too expensive
and time-consuming for clinical application. We thus propose a novel
data-augmented deep learning model for estimating stride width in
individuals with and without neurodegenerative disease using a minimal
set of wearable IMUs. Twelve patients with neurodegenerative, clinically
diagnosed Spinocerebellar ataxia type 3 (SCA3) performed over ground
walking trials, and seventeen healthy individuals performed treadmill
walking trials at various speeds and gait modifications while wearing
IMUs on each shank and the pelvis. Results demonstrated stride width
mean absolute errors of 3.3±0.7cm and 2.9±0.5cm for the
neurodegenerative and healthy groups, respectively, which were below the
minimal clinically important difference of 6.0cm. Stride width
variability mean absolute errors were 1.5cm and 0.8cm for
neurodegenerative and healthy groups, respectively. Data augmentation
significantly improved accuracy performance in the neurodegenerative
group, likely because they exhibited larger variations in walking
kinematics as compared with healthy subjects. These results could enable
clinically meaningful and accurate portable stride width monitoring for
individuals with and without neurodegenerative disease, potentially
enhancing rehabilitative training, assessment, and dynamic balance
control in clinical and real-life settings.